↪︎ ICML 2023 Topological Deep Learning Challenge : Design and Results
The ICML 2023 Topological Deep Learning Challenge was a competition that aimed to benchmark and promote the implementation of topological deep learning methods. I implemented the SCoNe layer from the paper “Principled Simplicial Neural Networks for Trajectory Prediction” as part of this challenge, and placed second in the simplicial complex category. The implementation is now part of the TopoModelX library, and a tutorial notebook is available in the TopoModelX documentation.
Abstract
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two-month duration. This paper describes the design of the challenge and summarizes its main findings.
